---
title: OKR Generation Agent — Mark's AI Strategy Tool
type: article
created: '2026-02-26'
updated: '2026-02-26'
source_docs:
- raw/2026-02-26-review-of-draft-client-q2-okrs-125682121.md
tags:
- ai-tools
- okrs
- strategy
- account-management
- papertube
layer: 2
client_source: null
industry_context: null
transferable: true
---

# OKR Generation Agent — Mark's AI Strategy Tool

## Overview

Mark has built an AI agent that generates client OKR drafts by analyzing all available client data — SEO performance, ad accounts, website health, and historical work. The agent is designed to replace the manual, time-intensive process of writing quarterly OKRs from scratch, which the team consistently lacks bandwidth to complete.

The tool is not a one-shot generator. It's designed for iterative refinement through a human feedback loop, with account managers providing directional guidance (e.g., "more of this, less of that") that the agent integrates in a second pass.

## How It Works

### Data Sources

The agent ingests all available client signals:

- SEO data (rankings, technical health, content gaps)
- Paid ad performance
- Website analytics and structure
- Historical work and prior strategy documents

Based on this, it surfaces recommended focus areas for the quarter.

### Generation Process

1. **Mark runs the agent** for each client and posts the resulting OKR draft in Slack
2. **Account managers review** and provide directional feedback — no need for precise edits, just high-level guidance ("focus more on X," "deprioritize Y")
3. **Mark re-runs the agent** with the feedback incorporated
4. Iteration continues until the account manager is satisfied

Feedback can be sent as a Slack message or email — it does not need to be structured or technical.

## Scale: Agent Swarms

For high-volume use cases, the agent can be run as a **swarm of parallel instances**. In the meeting where this was discussed, 25 agents were running simultaneously to generate strategy documents for PaperTube's 200+ accounts — processing roughly one account every four minutes.

> "It's a crazy amount of research that you get in a short time." — Mark Hope

This swarm approach makes it feasible to produce strategy documentation at a scale that would be completely impractical manually. At the time of the Q2 OKR meeting, only ~50 of PaperTube's accounts had existing strategy docs; the swarm was filling in the remaining 150+.

## Why This Matters

The team had not completed manual OKRs for Q2 due to time constraints. The agent addresses this directly — it handles the first draft entirely, leaving account managers to apply their client relationship knowledge as a refinement layer rather than doing the full authoring work.

This also creates a reusable, scalable process: the same agent and feedback loop can be applied each quarter without rebuilding from scratch.

## Limitations and Human Judgment

The agent works from data signals, not relationship context. Account managers are expected to correct for:

- Recent strategic pivots the client has communicated verbally
- Relationship dynamics that affect what's realistic to propose
- Changes in client priorities since the last data sync (e.g., Citrus had shifted focus since prior OKRs were generated)

The agent's output is a starting point, not a final deliverable.

## Related

- [[clients/papertube/_index]] — primary test case for agent swarm at scale
- [[clients/citrus/_index]] — example of client whose priorities shifted, requiring feedback-loop correction
- [[knowledge/ai-tools/abm-factory]] — related AI automation tool for account-based marketing outreach
- [[knowledge/account-management/client-okr-process]] — broader OKR process context